PROJECT SUMMARY/ABSTRACT This Pathway to Independence Award application is submitted by a pulmonary and critical care epidemiologist committed to improving the quality of patient-oriented research for patients experiencing acute respiratory failure (ARF). Worldwide, millions of patients develop ARF annually. In the U.S., nearly one million patients with ARF require mechanical ventilation annually, accounting for a quarter of all intensive care unit (ICU) admissions. As improvements in ICU care reduce such patients? in-hospital mortality rates, attention has shifted to the challenges ARF survivors face in regaining their prior cognitive, physical, and psychosocial functioning. However, there is a key barrier for randomized clinical trials (RCTs) testing new interventions to improve ARF survivorship ? that is, the current lack of an endpoint that (1) captures long-term patient dispositions, (2) incorporates patient preferences and perspectives, and (3) is able to be analyzed without concern for statistical biases. The overarching goal of this research is to support clinical innovation by developing new approaches to measure and report long-term patient-centered outcomes that overcome the methodological barriers currently limiting ARF RCTs. The applicant will accomplish his goals under the mentorship of established researchers in critical care, patient-centered outcomes research, statistics, and informatics to assure his transition to a tenure-track faculty position in the R00 phase and his emergence as a leading pulmonary and critical care epidemiologist. First, the applicant will use an innovative combination of qualitative and quantitative research methods to elicit and integrate ARF survivors? and their caregivers? perspectives into a new patient-centered, long-term composite outcome measure (K99 phase). During the R00 phase, the applicant will recruit ARF survivors to participate in a prospective cohort, and follow these patients to describe the burden of ARF survivorship over 1-year using the new endpoint developed during the K99 phase. This endeavor will also provide key data that will facilitate sample size calculations in future ARF RCTs. Data from this cohort will additionally be used to develop an electronic health record (EHR)-based algorithm to predict risks for adverse long-term outcomes among ARF patients early in their ICU stays. Thus, this K99/R00 will augment ARF research by establishing a new outcome measure anchored in patient perspectives, improving the understanding and clinical prognostication of post-ICU morbidity following ARF, and facilitate the efficiency and clinical relevance of future ARF RCTs by enabling measurement of patients? baseline risks for different outcomes. Concurrently, the didactic work, individual study, and hands-on learning in mixed-methods research, natural language processing, and predictive analytics will fill key training gaps for the applicant, thereby positioning him for a successful, independently-funded research career advancing the science of outcomes measurement and analysis for ARF RCTs.